Figure 7. Changes in maximal rain rates averaged over all
events for durations of 10-min (a), 1 h (b), 6 h (c) and 24-h (d)
between future and historic simulations (future – historic).
Statistically significant differences are circumscribed in gray.
In contrast to the decrease in rainfall accumulation, and as exemplified
by the first case study (Sect. 3.1), regionally maximal 10-min rain
rates (maximum along all pixels and timesteps) in future simulations are
significantly higher than in historic simulations (Fig. 4c-d, Table 1)
with an average increase of 22%. This conclusion holds for all
sub-regions inspected here, except for the desert sub-region, in which
the increase (11%) is non-significant (Fig. 4d, Fig. S8, Table 1).
Increases of the regionally maximal 10-min rain rates over both the
Mediterranean climate and Sea sub-regions are on average
>21%, and the increase over land, as a result of the small
increase over the desert, is 18%. Furthermore, most of the events
(85%) have higher values in future compared to historic simulations and
this is rather consistent among the different sub-regions (Table 1). The
increase in regionally maximal rain rates between historic and future
simulations holds for longer durations as well (Fig. S9).
4 Summary and Discussion
This study shows the changes in rainfall patterns between paired
simulations of historic and future HPEs, with the objective of
identifying whether common changes in rainfall patterns exist, and
characterizing these changes. The collection of objectively identified
41 HPEs was simulated twice, and the results of the two simulations are
compared. The first simulation is based on historic conditions, and the
second applies expected changes in various meteorological parameters
from the RCP 8.5 scenario for the end of the 21stcentury on top of historic initial and boundary conditions. Selected
events represent some of the heaviest precipitation events in the region
around the end of the 20th century. Our results, shown
first for a case study, and then for the full collection of HPEs,
demonstrate the added value of using event-based simulations, and
provide high resolution projections of future changes in rainfall
patterns, highlighting the importance of changes in specific rainfall
constituents, as discussed below.
4.1 Opportunities Gained by the Event-Based Approach and their
Implications
Large-scale and long term CPM simulations are becoming increasingly
attainable, allowing to better characterize precipitation extremes in
future climate scenarios (e.g., Coppola et al., 2020; Kendon et al.,
2018). However, there are still difficulties in providing reliable
projections of rainfall during HPEs (Kendon et al., 2021); the
computational and the power consumption costs of these simulations are
huge (Fuhrer et al., 2018; Loft, 2020), and the rarest of extremes are
difficult to characterize even in runs extending for many years.
Therefore, if the purpose of a study is to identify potential changes in
only a subset of the climate, e.g., HPEs, a full-climate run should be
used prudently.
Here, using an event-based approach we were able to show plausible
impacts of climate change on some of the heaviest rainstorms in the
eastern Mediterranean. Furthermore, we show that many “plausible”
instances (i.e., individual HPE events) point in the same direction;
therefore, the plausible scenario may be considered as the probable
scenario. Even if the entire variance of possibilities is not perfectly
represented using this method, the emerging similar response enables us
to garner insight on “climate questions”, such as projections
of future precipitation patterns, using a weather model. We
showed that rainfall accumulation under global warming conditions
decreases over > 90% of the simulated HPEs and analyzed
the properties of rainfall accounting for this decrease. The rain area
exhibits the largest and most consistent decrease and is heavily
associated with the decrease in rainfall accumulation, while increased
conditional rain rate is only weakly related to rainfall accumulation
and cannot counteract the decreased rain area.
The simulated change in rainfall patterns can have considerable
implications both on water resources and on natural hazards, which can
be illuminated if we focus on specific rainstorms. For example, event
#8 (1-7 Nov 1994) is an infamous ARST storm in which more than 500
people lost their lives, and extensive floods and damages occurred in
Egypt and Israel (Krichak et al., 2000; De Vries et al., 2013). This
event shows a substantial reduction in total rainfall under
future-simulated conditions (-51%; Fig. 5, Fig. S10). Such a reduction
would probably lead to a reduced risk of flash flooding, especially at
the northern part of the region. However, while in many places total
rainfall decreased in the simulation, few high-intensity rain cells
still impacted the Sinai desert (Movie S2), with total rainfall of
>100 mm, which would undoubtedly cause substantive floods
in this region.
HPE #12 (Fig. S11) triggered a major streamflow increase and raised the
level of the Sea of Galilee, the largest surficial freshwater reservoir
in the region, by 45 cm within a week (compared with <10 cm
rise the week before the storm occurred). This rise is equivalent to the
yearly industrial consumption of freshwater in Israel at that time
(~90 106 m3) and
constitutes more than a fifth of the annual water rise of the lake. The
simulation of the future event indicates a substantial decrease in total
rainfall (-27%). As the hydrological response to decreases in rainfall
is non-linear (e.g., Peleg et al., 2014), this would probably lead to an
even larger decrease in freshwater recharge with major implications on
water resources. While a hydrological simulation of the different events
is out of the scope of this paper, we stress that to have better
insights about the hydrological response, a comparison of historic and
future simulations of specific events through a hydrological model is
highly desired.
It is important to note that the frequency of events (e.g., Myhre et
al., 2019) is not implicitly considered in our simulations. Rain events
in the region are projected to have a reduced frequency
(~-20%; Hochman et al., 2018; Zappa et al., 2015), and
thus, the decreased rainfall we show here for the specific simulated
events, may be considered as an underestimation of the projected changes
in total precipitation from HPEs accounting for event occurrences.
Nevertheless, a minor shortcoming of the PGW methodology is that
frequency data is not totally excluded from the applied changes, which
arise from the climatology of 25 years of CMIP5 models’ simulations. For
this reason, changes in specific properties of events should be
reflected by the mean climatology. Meaning that if our simulations would
constitute a large portion of a 25-yr time interval, they would affect
the mean climatology as well. Forty-one HPEs, however, are not a
substantial part of the climatological mean of 25 years
(~3% of the days in the season we examine
[Oct-Apr]), and thus our simulations are not expected to be severely
biased by this issue.
A potential limitation that this study can be criticized for is the use
of a single climate scenario forcing for the PGW and as such it will
give only plausible results, rather probable. However, (a) this single
scenario is the ensemble mean of CMIP5 models, which can be considered
as a best estimate, to date, of large scale future changes, though work
currently in progress shows that CMIP6 models generally simulate
similar, and if anything more severe, changes to CMIP5 in this region
(not shown), (b) we use a collection of many objectively-identified
events that constitutes some of the highest magnitude HPEs in the
region. Results for this large set of paired-simulations show a similar
behavior of different events representing different synoptic-scale
conditions. Therefore, we claim that the sign and magnitude of the
changes that emerge from these simulations should be considered as a
probable projection of HPEs in the region.
Indeed, the PGW event-based methodology provides us with projections for
HPEs in a warmer climate. However, it must be noted we do not attempt to
provide a climatology of HPEs in the future, nor give updated extreme
event levels and frequencies. While these can be obtained using a
framework which accounts for the frequency of events (Marra et al.,
2019), the results we obtain have significance in drawing possible
future scenarios for some of the heaviest precipitation events in the
region. High resolution rainfall projections can also help improving
future predictions in approaches requiring a changed rainfall
distribution (e.g., Marra et al., 2021).
4.2 Changes in Rainfall Patterns During Rainstorms
Future rainstorms simulated in this work show quite a difference in
rainfall patterns compared to historic rainstorms, mainly being more
concentrated in both space and time. Given that the conditional rain
rate increases, one might expect an increase in total precipitation
during heavy precipitation events, as projected, for example, over
Europe (e.g., Y. Chen et al., 2020; Hawcroft et al., 2018; Kendon et
al., 2014). However, two other factors, less often addressed, negatively
affect total rainfall: the size of the rain area, and the duration of
the events. Among these two, we find that the
rain area is the main contributor
to decreased rainfall accumulation, which decreases, on average, by
40%. Furthermore, the rain area has a high correlation with the changes
in rainfall accumulation, while the event duration decreases on average
by 9% and has a low correlation with rainfall accumulation changes.
It must be noted, however, that the changes in the rain area are not
constant over different rain rates thresholds. The baseline 0.1 mm
h-1 intensity is a good proxy for the total storm
area. Going to larger thresholds, the area represented is a better
indicator for the intense “core” of the storm, namely the inner part
of convective cells during the storm. In fact, we found an increase in
the rain area for thresholds of >10 mm
hr-1. This means that, although the total rain area of
HPEs shrinks, their cores are getting larger in future simulations.
Similar findings were reported by Peleg et al. (2018) using historic
radar observations over the eastern Mediterranean and by Wasko et al.
(2016) using rain gauges in Australia. Both studies showed that total
rain area and the convective core area scale with temperature in
opposite directions: total area exhibits a negative scaling, while the
area of the convective cores is positively scaled with temperature; this
is probably related to an enhanced moisture convergence into the
convective cores from the total storm extent. In contrast, results from
studies of future extreme precipitation in the Netherlands and in the UK
show the area of the storms is expected to increase with global warming
(Y. Chen et al., 2020; Lochbihler et al., 2017, 2019), which may
indicate a regional dependence in the scaling of the rain area, but this
topic should be addressed in future studies (Fowler, Lenderink, et al.,
2021).
Since the hydrological response to HPEs is heavily related to space-time
precipitation characteristics, the results shown above would have an
immense impact on the hydrology of future rainstorms. Larger storm
cores, having increased short duration rain rates may increase the risk
of urban flooding and short-lived, fast responding flash floods (e.g.,
Tarasova et al., 2019), as well as soil erosion (e.g., Shmilovitz et
al., 2021). However, this effect is expected to be mitigated by the
decreased rainfall frequency caused by the shorter storm duration and
smaller overall area. Combined, a possible conclusion could be that over
the affected (rainy) area, the risk of short-duration natural hazards is
higher, while over the entire domain this is uncertain. Yet, a clearer
conclusion can be drawn for the detrimental effects of the changes in
rainfall patterns over the entire storm through longer-duration
processes: mean rain rates and amounts are expected to dramatically
decrease. Therefore, the expected hydrological impact would include a
further reduction of streamflow and a decline in freshwater resources,
which requires immediate address by policy makers.
Two key aspects are missing from the results presented here: a detailed
analysis of the meteorological factors affecting the modeled change in
rainfall patterns and their scaling with temperature, and a modeling of
the hydrological impact of these changes. These two prospective aspects
are currently being further studied. We call for a continued use of the
PGW methodology as a relatively easy-to-implement experiment, with
results relevant to events of specific interest such as HPEs.
5 Conclusions
Through high-resolution event-based simulations of eastern Mediterranean
HPEs in present and future climate, we show that in future: (a) event
rainfall accumulations decrease substantially (inter-event average =
-30%), throughout the study region, (b) mean conditional rain rate is
increased (+15%), (c) event duration is getting shorter (-9%), and (d)
rain area becomes dramatically smaller (-40%). The areal coverage for
various rain rates shows opposing changes for lower and higher rain
rates: it is reduced for low rain rate thresholds, and expanded for high
rate thresholds. Thus, rainstorms become more concentrated in future
simulations, with convective cores that exhibit shorter autocorrelation
distance and higher regionally maximal rain rates (+22%). Furthermore,
some increases in local short duration rain rates are seen mostly over
the coastal region, but long duration rain rates are decreased
throughout the region. The changes found are rather consistent across
events, suggesting that these event-based conclusions may actually be
probable. Changes in rainfall properties identified here reveal the
dominance of the rain area in determining the decrease in total
rainfall, with great implications over future hydrological processes.
Acknowledgments, Samples, and Data
The authors thank Y. Shmilovitz and R. Dann for both fruitful
discussions and help with coding issues. This research has been
supported by the Israel Ministry of Science and Technology (grant no.
61792) and the Israel Water Authority. Shacham radar data for the 41
HPEs are available online
(https://doi.org/10.5281/zenodo.5353714). ERA-Interim data were
downloaded from the Research Data Archive at the National Center for
Atmospheric Research, Computational and Information Systems Laboratory
(https://doi.org/10.5065/D6CR5RD9). CMIP5 data were downloaded
from the ESGF Node at DKRZ
(https://esgf-data.dkrz.de/projects/esgf-dkrz/tou). The WRF
namelist.input file can be found in the Supporting Information. FM was
supported by the Institute of Atmospheric Sciences and Climate (ISAC) of
the National Research Council of Italy (CNR).
References
Alpert, P., & Shay-EL, Y. (1994). The moisture Source for the Winter
Cyclones in the Eastern Mediterranean. Israel Meteorological
Research Papers , 5 , 20–27.
Alpert, P., Ben-Gai, T., Baharad, A., Benjamini, Y., Yekutieli, D.,
Colacino, M., et al. (2002). The paradoxical increase of Mediterranean
extreme daily rainfall in spite of decrease in total values.Geophysical Research Letters , 29 (11), 1536.
https://doi.org/10.1029/2001GL013554
Armon, M., Dente, E., Smith, J. A., Enzel, Y., & Morin, E. (2018).
Synoptic-scale control over modern rainfall and flood patterns in the
Levant drylands with implications for past climates. Journal of
Hydrometeorology , 19 (6), 1077–1096.
https://doi.org/10.1175/JHM-D-18-0013.1
Armon, M., Morin, E., & Enzel, Y. (2019). Overview of modern
atmospheric patterns controlling rainfall and floods into the Dead Sea:
Implications for the lake’s sedimentology and paleohydrology.Quaternary Science Reviews , 216 , 58–73.
https://doi.org/10.1016/j.quascirev.2019.06.005
Armon, M., Marra, F., Enzel, Y., Rostkier-Edelstein, D., & Morin, E.
(2020). Radar-based characterisation of heavy precipitation in the
eastern Mediterranean and its representation in a convection-permitting
model. Hydrology and Earth System Sciences , 24 (3),
1227–1249. https://doi.org/10.5194/hess-24-1227-2020
Ashbel, D. (1938). Great floods in Sinai Peninsula, Palestine, Syria and
the Syrian Desert, and the influence of the Red Sea on their formation.Quarterly Journal of the Royal Meteorological Society ,64 (277), 635–639.
Bacchi, B., & Ranzi, R. (1996). On the derivation of the areal
reduction factor of storms. Atmospheric Research ,42 (1–4), 123–135. https://doi.org/10.1016/0169-8095(95)00058-5
Ban, N., Schmidli, J., & Schär, C. (2014). Evaluation of the
convection-resolving regional climate modeling approach in decade-long
simulations. Journal of Geophysical Research , 119 (13),
7889–7907. https://doi.org/10.1002/2014JD021478
Belachsen, I., Marra, F., Peleg, N., & Morin, E. (2017). Convective
rainfall in dry climate: relations with synoptic systems and flash-flood
generation in the Dead Sea region. Geophysical Research Abstracts
EGU General Assembly . https://doi.org/10.5194/hess-2017-235
Borga, M., Stoffel, M., Marchi, L., Marra, F., & Jakob, M. (2014).
Hydrogeomorphic response to extreme rainfall in headwater systems: Flash
floods and debris flows. Journal of Hydrology , 518 (PB),
194–205. https://doi.org/10.1016/j.jhydrol.2014.05.022
Brogli, R., Kröner, N., Sørland, S. L., Lüthi, D., & Schär, C. (2019).
The role of hadley circulation and lapse-rate changes for the future
European summer climate. Journal of Climate , 32 (2),
385–404. https://doi.org/10.1175/JCLI-D-18-0431.1
Cannon, A. J., & Innocenti, S. (2019). Projected intensification of
sub-daily and daily rainfall extremes in convection-permitting climate
model simulations over North America: Implications for future
intensity-duration-frequency curves. Natural Hazards and Earth
System Sciences , 19 (2), 421–440.
https://doi.org/10.5194/nhess-19-421-2019
Chan, S. C., Kendon, E. J., Giorgia, B., Elizabeth, F., & Fowler, H. J.
(2020). Europe-wide precipitation projections at convection permitting
scale with the Unified Model. Climate Dynamics ,(submitted (0123456789), 1–23.
https://doi.org/10.1007/s00382-020-05192-8
Chen, G., Wang, W.-C., Cheng, C.-T., & Hsu, H.-H. (2020). Extreme snow
events along the coast of the northeast United States: Potential changes
due to global warming. Journal of Climate , 1–46.
https://doi.org/10.1175/jcli-d-20-0197.1
Chen, J., Wang, Z., Tam, C. Y., Lau, N. C., Lau, D. S. D., & Mok, H. Y.
(2020). Impacts of climate change on tropical cyclones and induced storm
surges in the Pearl River Delta region using pseudo-global-warming
method. Scientific Reports , 10 (1), 1–10.
https://doi.org/10.1038/s41598-020-58824-8
Chen, Y., Paschalis, A., Kendon, E., Kim, D., & Onof, C. (2020).
Changing Spatial Structure of Summer Heavy Rainfall, Using
Convection‐Permitting Ensemble. Geophysical Research Letters ,
1–12. https://doi.org/10.1029/2020gl090903
Coppola, E., Sobolowski, S., Pichelli, E., Raffaele, F., Ahrens, B.,
Anders, I., et al. (2020). A first-of-its-kind multi-model
convection permitting ensemble for investigating convective phenomena
over Europe and the Mediterranean . Climate Dynamics (Vol. 55).
https://doi.org/10.1007/s00382-018-4521-8
Crook, J., Klein, C., Folwell, S., Taylor, C. M., Parker, D. J.,
Stratton, R., & Stein, T. (2019). Assessment of the Representation of
West African Storm Lifecycles in Convection-Permitting Simulations.Earth and Space Science , 6 (5), 818–835.
https://doi.org/10.1029/2018EA000491
Dayan, U., & Abramski, R. (1983). Heavy rain in the Middle East related
to unusual jet stream properties. Bulletin of the American
Meteorological Society , 64 (10), 1138–1140.
Dayan, U., & Morin, E. (2006). Flash flood – producing rainstorms over
the Dead Sea: A review. New Frontiers in Dead Sea
Paleoenvironmental Research: Geological Society of America Special
Paper , 401 (04), 53–62. https://doi.org/10.1130/2006.2401(04).
Dayan, U., Lensky, I. M., Ziv, B., & Khain, P. (2021). Atmospheric
conditions leading to an exceptional fatal flash flood in the Negev
Desert, Israel. Natural Hazards and Earth System Sciences ,21 (5), 1583–1597. https://doi.org/10.5194/nhess-21-1583-2021
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., et al. (2011). The ERA-Interim reanalysis: configuration
and performance of the data assimilation system. Quarterly Journal
of the Royal Meteorological Society , 137 (656), 553–597.
https://doi.org/10.1002/qj.828
Dirmeyer, P. A., Brubaker, K. L., & DelSole, T. (2009). Import and
export of atmospheric water vapor between nations. Journal of
Hydrology , 365 (1–2), 11–22.
https://doi.org/10.1016/j.jhydrol.2008.11.016
Donat, M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A., & Maher,
N. (2016). More extreme precipitation in the world’s dry and wet
regions. Nature Climate Change , 6 (5), 508–513.
https://doi.org/10.1038/nclimate2941
El‐Fandy, M. G. (1946). Barometric lows of cyprus. Quarterly
Journal of the Royal Meteorological Society , 72 (314), 291–306.
https://doi.org/10.1002/qj.49707231406
Enzel, Y., Bookman (Ken Tor), R., Sharon, D., Gvirtzman, H., Dayan, U.,
Ziv, B., & Stein, M. (2003). Late Holocene climates of the Near East
deduced from Dead Sea level variations and modern regional winter
rainfall. Quaternary Research , 60 (3), 263–273.
https://doi.org/10.1016/j.yqres.2003.07.011
Fatichi, S., Ivanov, V. Y., Paschalis, A., Peleg, N., Molnar, P.,
Rimkus, S., et al. (2016). Uncertainty partition challenges the
predictability of vital details of climate change. Earth’s
Future , 4 (5), 240–251. https://doi.org/10.1002/2015EF000336
Ferreira, R. N. (2021). Cut-Off Lows and Extreme Precipitation in
Eastern Spain : Current and Future Climate.
Flaounas, E., Röthlisberger, M., Boettcher, M., Sprenger, M., & Wernli,
H. (2021). Extreme wet seasons – their definition and relationship with
synoptic-scale weather systems. Weather and Climate Dynamics ,2 (1), 71–88. https://doi.org/10.5194/wcd-2-71-2021
Fosser, G., Khodayar, S., & Berg, P. (2014). Benefit of convection
permitting climate model simulations in the representation of convective
precipitation. Climate Dynamics , 44 (1–2), 45–60.
https://doi.org/10.1007/s00382-014-2242-1
Fowler, H. J., Lenderink, G., Prein, A. F., Westra, S., Allan, R. P.,
Ban, N., et al. (2021). Anthropogenic intensification of short-duration
rainfall extremes. Nature Reviews Earth & Environment ,2 (2), 107–122. https://doi.org/10.1038/s43017-020-00128-6
Fowler, H. J., Wasko, C., & Prein, A. F. (2021). Intensification of
short-duration rainfall extremes and implications for flood risk:
Current state of the art and future directions. Philosophical
Transactions of the Royal Society A: Mathematical, Physical and
Engineering Sciences , 379 (2195).
https://doi.org/10.1098/rsta.2019.0541
Fowler, H. J., Ali, H., Allan, R. P., Ban, N., Barbero, R., Berg, P., et
al. (2021). Towards advancing scientific knowledge of climate change
impacts on short-duration rainfall extremes. Philosophical
Transactions of the Royal Society A: Mathematical, Physical and
Engineering Sciences , 379 (2195).
https://doi.org/10.1098/rsta.2019.0542
Fuhrer, O., Chadha, T., Hoefler, T., Kwasniewski, G., Lapillonne, X.,
Leutwyler, D., et al. (2018). Near-global climate simulation at 1km
resolution: Establishing a performance baseline on 4888 GPUs with COSMO
5.0. Geoscientific Model Development , 11 (4), 1665–1681.
https://doi.org/10.5194/gmd-11-1665-2018
Garfinkel, C. I., Adam, O., Morin, E., Enzel, Y., Elbaum, E., Bartov,
M., et al. (2020). The Role of Zonally Averaged Climate Change in
Contributing to Intermodel Spread in CMIP5 Predicted Local Precipitation
Changes. Journal of Climate , 33 (3), 1141–1154.
https://doi.org/10.1175/JCLI-D-19-0232.1
Giorgi, F., & Lionello, P. (2008). Climate change projections for the
Mediterranean region. Global and Planetary Change ,63 (2–3), 90–104.
https://doi.org/10.1016/j.gloplacha.2007.09.005
Goldreich, Y. (1994). The spatial distribution of annual rainfall in
Israel - a review. Theoretical and Applied Climatology ,50 (1–2), 45–59. https://doi.org/10.1007/BF00864902
Goldreich, Yair. (1995). Temporal variations of rainfall in Israel.Climate Research , 5 (2), 167–179.
https://doi.org/10.3354/cr005167
Goldreich, Yair. (2012). The climate of Israel: observation,
research and application . Springer Science & Business Media.
https://doi.org/10.1007/978-1-4615-0697-3
Gómez-Navarro, J. J., Raible, C. C., García-Valero, J. A., Messmer, M.,
Montávez, J. P., & Martius, O. (2019). Event selection for dynamical
downscaling: a neural network approach for physically-constrained
precipitation events. Climate Dynamics .
https://doi.org/10.1007/s00382-019-04818-w
Gutmann, E. D., Rasmussen, R. M., Liu, C., Ikeda, K., Bruyere, C. L.,
Done, J. M., et al. (2018). Changes in hurricanes from a 13-Yr
convection-permitting pseudo- global warming simulation. Journal
of Climate , 31 (9), 3643–3657.
https://doi.org/10.1175/JCLI-D-17-0391.1
Hawcroft, M., Walsh, E., Hodges, K., & Zappa, G. (2018). Significantly
increased extreme precipitation expected in Europe and North America
from extratropical cyclones. Environmental Research Letters ,13 (12). https://doi.org/10.1088/1748-9326/aaed59
Hochman, A., Harpaz, T., Saaroni, H., & Alpert, P. (2018). The seasons’
length in 21st century CMIP5 projections over the eastern Mediterranean.International Journal of Climatology , (December 2017), 1–11.
https://doi.org/10.1002/joc.5448
Israel, A. of. (2011). The new Atlas of Israel: the national
atlas . Jerusalem: Survey of Israel ; The Hebrew University of
Jerusalem.
Kahana, R., Ziv, B., Enzel, Y., & Dayan, U. (2002). Synoptic
climatology of major floods in the Negev Desert, Israel.International Journal of Climatology , 22 (7), 867–882.
https://doi.org/10.1002/joc.766
Kawase, H., Yoshikane, T., Hara, M., Kimura, F., Yasunari, T., Ailikun,
B., et al. (2009). Intermodel variability of future changes in the Baiu
rainband estimated by the pseudo global warming downscaling method.Journal of Geophysical Research Atmospheres , 114 (24),
1–14. https://doi.org/10.1029/2009JD011803
Keller, M., Kröner, N., Fuhrer, O., Lüthi, D., Schmidli, J., Stengel,
M., et al. (2018). The sensitivity of alpine summer convection to
surrogate climate change: An intercomparison between
convection-parameterizing and convection-resolving models.Atmospheric Chemistry and Physics , 18 (8), 5253–5264.
https://doi.org/10.5194/acp-18-5253-2018
Kendon, E. J., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S.
C., & Senior, C. A. (2014). Heavier summer downpours with climate
change revealed by weather forecast resolution model. Nature
Climate Change , 4 (7), 570–576.
https://doi.org/10.1038/nclimate2258
Kendon, E. J., Blenkinsop, S., & Fowler, H. J. (2018). When will we
detect changes in short-duration precipitation extremes? Journal
of Climate , 31 (7), 2945–2964.
https://doi.org/10.1175/JCLI-D-17-0435.1
Kendon, E. J., Prein, A. F., Senior, C. A., & Stirling, A. (2021).
Challenges and outlook for convection-permitting climate modelling.Philosophical Transactions of the Royal Society A: Mathematical,
Physical and Engineering Sciences .
https://doi.org/10.1098/rsta.2019.0547
Krichak, S. O., Tsidulko, M., & Alpert, P. (2000). November 2, 1994,
severe storms in the southeastern Mediterranean. Atmospheric
Research , 53 (1–3), 45–62.
https://doi.org/10.1016/S0169-8095(99)00045-9
Kushnir, Y., Dayan, U., Ziv, B., Morin, E., & Enzel, Y. (2017). Climate
of the Levant: Phenomena and Mechanisms. In Y. Enzel & B.-Y. Ofer
(Eds.), Quaternary of the Levant: Environments, Climate Change,
and Humans (pp. 31–44). Cambridge, UK: Cambridge University Press.
Li, J., Wasko, C., Johnson, F., Evans, J. P., & Sharma, A. (2018). Can
Regional Climate Modeling Capture the Observed Changes in Spatial
Organization of Extreme Storms at Higher Temperatures? Geophysical
Research Letters , 45 (9), 4475–4484.
https://doi.org/10.1029/2018GL077716
Lochbihler, K., Lenderink, G., & Siebesma, A. P. (2017). The spatial
extent of rainfall events and its relation to precipitation scaling.Geophysical Research Letters , 44 (16), 8629–8636.
https://doi.org/10.1002/2017GL074857
Lochbihler, K., Lenderink, G., & Siebesma, A. P. (2019). Response of
Extreme Precipitating Cell Structures to Atmospheric Warming.Journal of Geophysical Research: Atmospheres , 124 (13),
6904–6918. https://doi.org/10.1029/2018JD029954
Loft, R. (2020). Earth system modeling must become more energy
efficient. Eos . https://doi.org/10.1029/2020EO147051
Marra, F., & Morin, E. (2015). Use of radar QPE for the derivation of
Intensity–Duration–Frequency curves in a range of climatic regimes.Journal of Hydrology , 531 , 427–440.
https://doi.org/10.1016/j.jhydrol.2015.08.064
Marra, F., & Morin, E. (2018). Autocorrelation structure of convective
rainfall in semiarid-arid climate derived from high-resolution X-Band
radar estimates. Atmospheric Research , 200 (September
2017), 126–138. https://doi.org/10.1016/j.atmosres.2017.09.020
Marra, F., Zoccatelli, D., Armon, M., & Morin, E. (2019). A simplified
MEV formulation to model extremes emerging from multiple nonstationary
underlying processes. Advances in Water Resources , 127 ,
280–290. https://doi.org/10.1016/j.advwatres.2019.04.002
Marra, F., Armon, M., Adam, O., Zoccatelli, D., Gazal, O., Garfinkel,
C. I., et al. (2021). Towards narrowing uncertainty in future
projections of local extreme precipitation. Geophysical Research
Letters , Accepted .
Meredith, E. P., Ulbrich, U., & Rust, H. W. (2020). Subhourly rainfall
in a convection-permitting model. Environmental Research Letters ,15 (3). https://doi.org/10.1088/1748-9326/ab6787
Morin, E. (2011). To know what we cannot know: Global mapping of minimal
detectable absolute trends in annual precipitation. Water
Resources Research , 47 (7), 1–9.
https://doi.org/10.1029/2010WR009798
Morrison, A., Villarini, G., Zhang, W., & Scoccimarro, E. (2019).
Projected changes in extreme precipitation at sub-daily and daily time
scales. Global and Planetary Change , 182 (May), 103004.
https://doi.org/10.1016/j.gloplacha.2019.103004
Moustakis, Y., Papalexiou, S. M., Onof, C. J., & Paschalis, A. (2021).
Seasonality, Intensity, and Duration of Rainfall Extremes Change in a
Warmer Climate. Earth’s Future , 9 (3), 1–15.
https://doi.org/10.1029/2020EF001824
Myhre, G., Alterskjær, K., Stjern, C. W., Hodnebrog, Marelle, L.,
Samset, B. H., et al. (2019). Frequency of extreme precipitation
increases extensively with event rareness under global warming.Scientific Reports , 9 (1), 1–10.
https://doi.org/10.1038/s41598-019-52277-4
Nasta, P., Adane, Z., Lock, N., Houston, A., & Gates, J. B. (2018).
Links between episodic groundwater recharge rates and rainfall events
classified according to stratiform-convective storm scoring: A
plot-scale study in eastern Nebraska. Agricultural and Forest
Meteorology , 259 (February), 154–161.
https://doi.org/10.1016/j.agrformet.2018.05.003
Nerini, D., Besic, N., Sideris, I., Germann, U., & Foresti, L. (2017).
A non-stationary stochastic ensemble generator for radar rainfall fields
based on the short-space Fourier transform. Hydrology and Earth
System Sciences , 21 (6), 2777–2797.
https://doi.org/10.5194/hess-21-2777-2017
O’Gorman, P. A. (2015). Precipitation Extremes Under Climate Change.Current Climate Change Reports , 1 (2), 49–59.
https://doi.org/10.1007/s40641-015-0009-3
Peleg, N., & Morin, E. (2012). Convective rain cells: Radar-derived
spatiotemporal characteristics and synoptic patterns over the eastern
Mediterranean. Journal of Geophysical Research: Atmospheres ,117 (15), 1–17. https://doi.org/10.1029/2011JD017353
Peleg, N., Ben-Asher, M., & Morin, E. (2013). Radar subpixel-scale
rainfall variability and uncertainty: Lessons learned from observations
of a dense rain-gauge network. Hydrology and Earth System
Sciences , 17 (6), 2195–2208.
https://doi.org/10.5194/hess-17-2195-2013
Peleg, N., Bartov, M., & Morin, E. (2014). CMIP5-predicted climate
shifts over the East Mediterranean: Implications for the transition
region between Mediterranean and semi-arid climates. International
Journal of Climatology , 2153 (July 2014), 2144–2153.
https://doi.org/10.1002/joc.4114
Peleg, N., Marra, F., Fatichi, S., Molnar, P., Morin, E., Sharma, A., &
Burlando, P. (2018). Intensification of convective rain cells at warmer
temperatures observed from high-resolution weather radar data.Journal of Hydrometeorology , JHM-D-17-0158.1.
https://doi.org/10.1175/JHM-D-17-0158.1
Pfahl, S., O’Gorman, P. A., & Fischer, E. M. (2017). Understanding the
regional pattern of projected future changes in extreme precipitation.Nature Climate Change , 7 (6), 423–427.
https://doi.org/10.1038/nclimate3287
Picard, L., & Mass, C. (2017). The sensitivity of orographic
precipitation to flow direction: An idealized modeling approach.Journal of Hydrometeorology , 18 (6), 1673–1688.
https://doi.org/10.1175/JHM-D-16-0209.1
Pichelli, E., Coppola, E., Sobolowski, S., Ban, N., Giorgi, F., Stocchi,
P., et al. (2021). The first multi-model ensemble of regional climate
simulations at kilometer-scale resolution part 2: historical and future
simulations of precipitation. Climate Dynamics ,56 (11–12), 3581–3602.
https://doi.org/10.1007/s00382-021-05657-4
Poujol, B., Prein, A. F., & Newman, A. J. (2020). Kilometer-scale
modeling projects a tripling of Alaskan convective storms in future
climate. Climate Dynamics , (0123456789).
https://doi.org/10.1007/s00382-020-05466-1
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen,
K., et al. (2015). A review on regional convection-permitting climate
modeling: Demonstrations, prospects, and challenges. Reviews of
Geophysics , 53 (2), 323–361.
https://doi.org/10.1002/2014RG000475
Prein, A. F., Rasmussen, R. M., Ikeda, K., Liu, C., Clark, M. P., &
Holland, G. J. (2017). The future intensification of hourly
precipitation extremes. Nature Climate Change , 7 (1),
48–52. https://doi.org/10.1038/nclimate3168
Rasmussen, R., Liu, C., Ikeda, K., Gochis, D., Yates, D., Chen, F., et
al. (2011). High-resolution coupled climate runoff simulations of
seasonal snowfall over Colorado: A process study of current and warmer
climate. Journal of Climate , 24 (12), 3015–3048.
https://doi.org/10.1175/2010JCLI3985.1
Raveh-Rubin, S., & Wernli, H. (2016). Large-scale wind and
precipitation extremes in the Mediterranean: dynamical aspects of five
selected cyclone events. Quarterly Journal of the Royal
Meteorological Society , 142 (701), 3097–3114.
https://doi.org/10.1002/qj.2891
Rinat, Y., Marra, F., Armon, M., Metzger, A., Levi, Y., Khain, P., et
al. (2020). Hydrometeorological analysis and forecasting of a 3-day
flash-flood-triggering desert rainstorm. Natural Hazards and Earth
System Sciences Discussions , 2020 , 1–35.
https://doi.org/10.5194/nhess-2020-189
Romine, G. S., Schwartz, C. S., Snyder, C., Anderson, J. L., & Weisman,
M. L. (2013). Model Bias in a Continuously Cycled Assimilation System
and Its Influence on Convection-Permitting Forecasts. Monthly
Weather Review , 141 (4), 1263–1284.
https://doi.org/10.1175/MWR-D-12-00112.1
Rostkier-Edelstein, D., Liu, Y., Wu, W., Kunin, P., Givati, A., & Ge,
M. (2014). Towards a high-resolution climatography of seasonal
precipitation over Israel. International Journal of Climatology ,34 (6), 1964–1979. https://doi.org/10.1002/joc.3814
Rubin, S., Ziv, B., & Paldor, N. (2007). Tropical Plumes over Eastern
North Africa as a Source of Rain in the Middle East. Monthly
Weather Review , 135 (12), 4135–4148.
https://doi.org/10.1175/2007MWR1919.1
Samuels, R., Rimmer, A., & Alpert, P. (2009). Effect of extreme
rainfall events on the water resources of the Jordan River.Journal of Hydrology , 375 (3–4), 513–523.
https://doi.org/10.1016/j.jhydrol.2009.07.001
Samuels, R., Hochman, A., Baharad, A., Givati, A., Levi, Y., Yosef, Y.,
et al. (2017). Evaluation and projection of extreme precipitation
indices in the Eastern Mediterranean based on CMIP5 multi-model
ensemble. International Journal of Climatology .
https://doi.org/10.1002/joc.5334
Sato, T., Kimura, F., & Kitoh, A. (2007). Projection of global warming
onto regional precipitation over Mongolia using a regional climate
model. Journal of Hydrology , 333 (1), 144–154.
https://doi.org/10.1016/j.jhydrol.2006.07.023
Schär, C., Frei, C., Lüthi, D., & Davies, H. C. (1996). Surrogate
climate-change scenarios for regional climate models. Geophysical
Research Letters , 23 (6), 669–672.
https://doi.org/10.1029/96GL00265
Schwartz, C. S., Romine, G. S., Sobash, R. A., Fossell, K. R., &
Weisman, M. L. (2015). NCAR’s Experimental Real-Time Convection-Allowing
Ensemble Prediction System. Weather and Forecasting ,30 (6), 1645–1654. https://doi.org/10.1175/WAF-D-15-0103.1
Shepherd, T. G. (2014). Atmospheric circulation as a source of
uncertainty in climate change projections. Nature Geoscience ,7 (10), 703–708. https://doi.org/10.1038/NGEO2253
Shepherd, T. G. (2019). Storyline approach to the construction of
regional climate change information. Proceedings of the Royal
Society A: Mathematical, Physical and Engineering Sciences ,475 (2225). https://doi.org/10.1098/rspa.2019.0013
Shmilovitz, Y., Marra, F., Wei, H., Argaman, E., Nearing, M., Goodrich,
D., et al. (2021). Frequency analysis of storm-scale soil erosion and
characterization of extreme erosive events by linking the DWEPP model
and a stochastic rainfall generator. Science of the Total
Environment , 787 , 147609.
https://doi.org/10.1016/j.scitotenv.2021.147609
Sillmann, J., Shepherd, T. G., van den Hurk, B., Hazeleger, W., Martius,
O., Slingo, J., & Zscheischler, J. (2021). Event-Based Storylines to
Address Climate Risk. Earth’s Future , 9 (2), 1–6.
https://doi.org/10.1029/2020EF001783
Tarasova, L., Merz, R., Kiss, A., Basso, S., Blöschl, G., Merz, B., et
al. (2019). Causative classification of river flood events. WIREs
Water , 6 (4), 1–23. https://doi.org/10.1002/wat2.1353
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012). An overview of
CMIP5 and the experiment design. Bulletin of the American
Meteorological Society . https://doi.org/10.1175/BAMS-D-11-00094.1
Taylor, R. G., Todd, M. C., Kongola, L., Maurice, L., Nahozya, E.,
Sanga, H., & Macdonald, A. M. (2013). Evidence of the dependence of
groundwater resources on extreme rainfall in East Africa. Nature
Climate Change , 3 (4), 374–378.
https://doi.org/10.1038/nclimate1731
Tebaldi, C., & Knutti, R. (2007). The use of the multi-model ensemble
in probabilistic climate projections. Philosophical Transactions
of the Royal Society A: Mathematical, Physical and Engineering
Sciences , 365 (1857), 2053–2075.
https://doi.org/10.1098/rsta.2007.2076
Trenberth, K. E., Fasullo, J. T., & Shepherd, T. G. (2015). Attribution
of climate extreme events. Nature Climate Change , 5 (8),
725–730. https://doi.org/10.1038/nclimate2657
Tubi, A., Dayan, U., & Lensky, I. M. (2017). Moisture transport by
tropical plumes over the Middle East: a 30-year climatology.Quarterly Journal of the Royal Meteorological Society ,143 (709), 3165–3176. https://doi.org/10.1002/qj.3170
De Vries, A. J., Tyrlis, E., Edry, D., Krichak, S. O., Steil, B., &
Lelieveld, J. (2013). Extreme precipitation events in the Middle East:
Dynamics of the Active Red Sea Trough. Journal of Geophysical
Research: Atmospheres , 118 (13), 7087–7108.
https://doi.org/10.1002/jgrd.50569
Warner, T. T. (2011). Numerical Weather and Climate Prediction .
https://doi.org/10.1017/CBO9780511763243
Wasko, C., Sharma, A., & Westra, S. (2016). Reduced spatial extent of
extreme storms at higher temperatures. Geophysical Research
Letters , 43 (8), 4026–4032. https://doi.org/10.1002/2016GL068509
Westra, S., Fowler, H. J., Evans, J. P., Alexander, L. V., Berg, P.,
Johnson, F., et al. (2014). Future changes to the intensity and
frequency of short-duration extreme rainfall. Reviews of
Geophysics , 52 , 522–555. https://doi.org/10.1002/2014RG000464
Zappa, G., Hawcroft, M. K., Shaffrey, L., Black, E., & Brayshaw, D. J.
(2015). Extratropical cyclones and the projected decline of winter
Mediterranean precipitation in the CMIP5 models. Climate
Dynamics , 45 (7–8), 1727–1738.
https://doi.org/10.1007/s00382-014-2426-8
Ziv, B., Dayan, U., Kushnir, Y., Roth, C., & Enzel, Y. (2006). Regional
and global atmospheric patterns governing rainfall in the southern
Levant. International Journal of Climatology , 26 (1),
55–73. https://doi.org/10.1002/joc.1238